Automatic estimation of digital image aesthetics by a computing device is used to support a wide variety of functionality. In an image curation example, digital image aesthetics estimation is used by the computing device to collect and group digital images that are visually pleasing. In an image search example, digital image aesthetics estimation is used by the computing device to rank digital images in a search result based on how visually pleasing the images are likely to appear to a user. Other examples include generation of creative recommendations and image editing suggestions. In this way, the estimation of digital image aesthetics may be used by a computing device to increase likelihood to providing an image result that is of interest to a user.
Conventional techniques employed by a computing device rely on a generic (e.g., universal) model to estimate digital image aesthetics. However, visual preferences may vary greatly from one user to another. A first user, for instance, may prefer lighting conditions, image scenes, and so forth that differ from that of a second user. Accordingly, these conventional techniques may lack accuracy when applied to a diverse range of users, which then has an effect on other functionality that relies on these techniques, such as image curation and image search as described above.